Parihar Suresh Dahake
@rknec.edu
Assistant Professor
Department of Management Technology, Shri Ramdeobaba College of Engineering and Management,Nagpur
EDUCATION
B.Tech,MBA,
RESEARCH INTERESTS
Marketing, Consumer Behavior, Retail Management, Marketing Communication
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Parihar Suresh Dahake, Prasad Bagaregari, and Nihar Suresh Dahake
IGI Global
To succeed in today's fast-paced retail industry, businesses must be able to predict their customers' actions. The goal of this study is to improve the accuracy of customer behaviour forecasts through the development of retail prediction analytics models. By applying state-of-the-art data analytics and machine learning methods, this study aims to understand better how to build and use predictive models that can foresee consumer behaviour, preference, and trend adoption. Researchers begin by looking at predictive analytics and how it may help the retail industry. It explains why retailers can't reliably forecast future customer behaviour using current data and analytics. The authors also examine some potential benefits of using more powerful prediction models. Some of the methods and algorithms studied in this study include those used for customer segmentation, sales forecasting, and churn prediction. It does this by performing a comprehensive study of the related literature.
Parihar Suresh Dahake, Rahul Vijay Mohare, and Nihar Suresh Dahake
IGI Global
This chapter looks at how Chat GPT (generative pre-trained transformer) technology can be used in a new way to make management education more accessible and more effective. Traditional management education often has trouble meeting the many different needs of students and giving them personalized help in real time, which can make learning less than ideal. In reaction, this study looks at how Chat GPT could change how management education is taught. The main goal of this study is to figure out how Chat GPT affects management education, focusing on how it affects how easy it is to learn and how well the education process works overall. Using Chat GPT's natural language processing features, students can get answers to their questions right away that are specific to them. This improves their learning experience and keeps them interested.
Parihar Suresh Dahake, Shravan Chandak, Rahul Vijay Mohare, Kanak Wadhwani, and Pritam Bhadade
IEEE
Predictive analytics is quickly changing the marketing industry. This is because predictive analytics allows marketers to use data-driven insights to understand better and talk to their customers. Predictive analytics, which can look at vast amounts of data, gives marketers a look into the future, like a crystal ball. This lets them predict how customers act, see new opportunities, and improve their marketing efforts.This piece talks about how predictive analytics can be used for customer acquisition, personalization, and engagement and the pros and cons of using it in marketing. We look at the latest changes in predictive analytics, such as methods for mood analysis, natural language processing, and machine learning. By using a lot of data, businesses can use predictive analytics to predict future trends, spot chances, and make their marketing efforts more effective. Predictive analytics, for example, can determine which customers are most likely to buy a particular product, which marketing platforms are best for reaching a specific demographic, and which promotions are most likely to boost sales. Through case studies and examples from different industries, we want to show how companies use predictive analytics to promote business growth, cut costs, and improve customer experiences. We also discuss the moral and privacy problems that come up with predictive analytics and give tips on using these methods. In the end, this article shows that predictive analytics is a technology that is changing marketing and that those who use it will be in a good situation to do well in the coming years. By using the power of predictive analytics, marketers can get a leg up on the competition, strengthen their relationships with clients, and more accurately and successfully reach their business goals.
Parihar Suresh Dahake, Rahul Vijay Mohare, and Nidhi Somani
IEEE
This research study investigates the use of machine learning algorithms to anticipate the behavior of online shoppers in e-commerce. The research uses a dataset containing various consumer factors, such as demographic information, previous purchases, and internet behavior. The subject of this research is the effectiveness of machine learning techniques such as random forests, support vector machines, and neural networks in predicting consumer behavior, such as purchase choices and product preferences. This is accomplished by the pre-processing of data, the selection of features, and the training of models. The findings indicate that the machine learning models accurately predicted consumer behavior with high accuracy and recall rates, with Random Forest beating the other investigated algorithms. The research shows several vital aspects, such as customer demographics, purchase history, and interaction with websites, significantly contributing to accurately forecasting consumer behavior. In conclusion, the research provides empirical evidence that machine learning algorithms can effectively predict customer behavior and improve personalized marketing methods in the e-commerce industry. The research results significantly affect businesses that want to employ data-driven insights to better consumer targeting and marketing activities.
Parihar Suresh Dahake and Saket Narendra Bansod
Inderscience Publishers